How Generative AI Is Altering Creative Work

Published on 26 Dec 2022

Generative, AI, Altering, Creative Work

Big words and pictures Generative A.I. models, also known as foundational models, have opened up new doors for companies and individuals engaged in content generation. In this blog, we’ll discuss how Generative AI alters creative work and how we create and perceive it. 

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What Is Generative AI?

As it is, generative A.I. is capable of a great deal. It may generate written and visual content such as blog entries, code, poetry, and even artwork (and even winning competitions, controversially). Predicting the next word in a sequence or the next picture from words describing previous images is made possible by the software's usage of advanced machine learning models. In 2017, Google Brain was the first to employ an LLM for context-aware text translation. Since then, several major tech companies, including Google (with its BERT and LaMDA models) and Facebook (with its OPT-175B and BlenderBot models), as well as the charity OpenAI, in which Microsoft has a majority stake, have developed significant generative AI natural language and text-to-image models (GPT-3 for text, DALL-E2 for images, and Whisper for speech). Open-source suppliers like HuggingFace and online communities like Mid journey (which contributed to the competition's victory) have also developed generative models.

Due to the vast quantity of data and computational resources needed for training these models, their application has been mostly limited to big technology corporations. GPT-3, for instance, used 45 terabytes of data during its first training and used 175 billion parameters or coefficients to produce predictions; a single training session for GPT-3 cost $12 million. The Chinese model, Wu Dao 2.0, includes 1.75 trillion variables. Companies often need more resources (data center capacity and cloud computing funds) to develop their models.

BioNeMo is a supercomputing-scale framework developed by NVIDIA for generative chemistry, proteomics, and DNA/RNA modeling. Once a generative model has been trained, however, it may be "fine-tuned" for a certain content domain with considerably less input. This has resulted in several domain-specific variants of BERT and GPT-3, such as those tailored to the biomedical field (BioBERT), the legal sector (Legal-BERT), and the French language (CamemBERT). OpenAI discovered that only 100 domain-specific samples significantly improved GPT-3's accuracy and relevance.

Human input is required at the outset and after the process to get the most out of generative AI applications.

In most cases, when people are given opportunities to express their creativity, they do so. For a generative model to generate material, however, a person must feed it some triggers. Until the following generation of an even more intelligent A.I. appears, "prompt engineer" will likely become a well-established career. An 82-page book containing DALL-E 2 picture prompts and a marketplace where users may purchase and sell prompts for a nominal charge have already resulted from this line of research. Most users will likely need to cycle through many options before getting the results they want from these systems.

It will then be up to a person to carefully review and alter the material that a model has generated. A single document may include the results from many prompts. It's possible that intensive processing is needed for image production. Winner of the Colorado "digitally modified photos" contest with the aid of Mid journey, Jason Allen, revealed to a reporter that he spent more than 80 hours creating more than 900 iterations of the artwork and refining his prompts over and over again. He then printed three works on the canvas after editing them in Adobe Photoshop and sharpening them using another A.I. program.

The diversity in generative A.I. models is staggering. Visuals, extended forms of text, emails, social media posts, audio recordings, code, and structured data are all accessible to them. They may generate original writing, translations, FAQs, sentiment analysis, summaries, and even video material.

The Use of Generative AI In the Creative World

Generative AI applications are capable of fulfilling multiple creative possibilities. These possibilities include:

Automated content creation: Large-scale linguistic and visual content production automation Articles, blog entries, and social media updates are just a few examples of the types of material that may be automatically generated using A.I. models. Businesses and professionals that often produce material may find this a helpful method for saving time.

Enhanced Content quality: Because A.I. models can learn from a great quantity of data and detect patterns that people may not be able to notice, the quality of material produced by A.I. may often be higher than that generated by humans. This has the potential to provide more reliable and useful information.

Diversified content kinds: More material forms can be generated by A.I. systems, not only text, photos, or video. This allows more varied and fascinating information to reach a bigger audience.

Customized materials: A.I. models may create unique materials for each user depending on their tastes. With this information, companies and professionals may produce material that is more likely to resonate with their intended audience, increasing the likelihood that it will be read and shared.

How Well Does This Tech Resemble Human Efforts In Creative Endeavors?

The following italicized phrase is a sample response to a sentence we authored by GPT-3, an OpenAI "large language model" (LLM). The pros and cons of most AI-produced material may be seen in the text written by GPT-3. First, it responds well to changes in the sentence prompts supplied; we explored several sentences before landing on that one. Second, the quality of the writing produced by the system is above average; there are no spelling or grammatical errors, and the words used are fitting. The third issue is that it needs to be edited; a numbered list at the top of this kind of article is not standard. At long last, it has generated suggestions that we had overlooked. To provide an example, we have yet to think of that final bit concerning tailored material.

It is a solid example of how useful these A.I. models may be for companies. They might have far-reaching effects on fields as diverse as advertising, computer science, design, the arts, media, and interpersonal communication, all of which are directly tied to the content production industry. To the untrained eye, this might pass for the "artificial general intelligence" humans have both longed for and dreaded.

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These few use cases for generative A.I. in business should make it apparent that we are only beginning to explore the possibilities it presents for businesses and their employees. We can scarcely fathom all the possibilities and ramifications; these A.I. models may produce, assuming they continue to advance as they have in their short existence. For instance, in the near future, these systems may be the norm for generating the first drafts of the vast majority of our written or image-based content. This includes emails, letters, articles, software programs, reports, blog entries, presentations, videos, etc. Improving in this area would have far-reaching and unexpected effects on issues like content ownership and I.P. protection. Still, it would also usher in a new era of discovery and innovation in the arts and sciences.


Featured image: Image by Freepik


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